Image Recognition

Your AVCodex agent can analyze images uploaded by users. This guide explains how image recognition works and how to dial it in for AV work.

When a user uploads an image, AVCodex processes it in one of two ways depending on the agent's model.

Vision-capable models (recommended)#

Models with native vision see images directly, the way a person would. The image is embedded in the conversation and the model can reference it naturally.

Vision-capable models:

Models WITHOUT vision:

• OpenAI o-series (o1, o1 Pro, o3, o3 Pro, o3-mini, o4 Mini).

Non-vision models (fallback)#

For models without native vision (the o-series reasoning models), AVCodex uses a separate image analysis tool powered by OpenAI's GPT-5 to describe the image, then passes that description to your agent's model.

That two-step process can lose context compared to native vision models. For most AV use cases (a tech pointing a phone camera at a screen full of small text and tiny LEDs), native vision is the better path.

1. Pick the right model#

For agents that lean on image analysis (rack photos, error code screens, drawings, label reading), pick a vision-capable model.

1. Open your agent in the AVCodex dashboard.
2. Go to Build > Configure.
3. Under Model, choose a vision-capable model like GPT-5 or Claude Sonnet 4.

2. Enable image recognition#

Make sure the capability is on:

1. Go to Build > Actions.
2. Find Image Recognition in the Basic Actions list.
3. Toggle it ON.

3. Image quality tips#

For best recognition accuracy:

• Resolution: higher-resolution images produce better results. A clear shot of a touch panel beats a tiny crop.
• Lighting: well-lit images are easier to analyze. Closet lighting can be rough on a phone camera, so flashlight on if needed.
• Focus: keep the subject in focus. Blurry SIMPL error text is hard for any model.
• Format: JPEG, PNG, and WebP are all supported.

4. Prompting for analysis#

Guide users to be specific about what they want analyzed.

Good prompts:

• "What error code is shown on this DM-NVX touch panel?"
• "Identify the model and serial of this Crestron rack-mount unit."
• "Read the text on this Q-SYS Designer fault dialog."
• "What signals are flowing into the DSP in this drawing?"

Vague prompts:

• "What is this?"
• "Tell me about the image."

If you notice differences between your AVCodex agent and ChatGPT's native image analysis:

1. Model selection: ChatGPT uses GPT-5 by default. Make sure your AVCodex agent uses GPT-5 or a comparable vision model like Claude Sonnet 4.
2. System prompt: your agent's role and instructions affect how it interprets images. ChatGPT has different defaults.
3. Context window: ChatGPT handles conversation context differently. For complex multi-image analysis (multiple rack angles, multi-page drawings), results can vary.

Images not being analyzed#

• Verify Image Recognition is enabled in the agent's Actions.
• Check that the user is uploading supported formats (JPEG, PNG, WebP, GIF).
• Make sure images are from supported sources (direct uploads, not every external URL).

Poor accuracy#

• Switch to a vision-capable model (GPT-5, Claude Sonnet 4, etc.).
• Ask users to provide clearer, higher-resolution images. Encourage close-ups for label and serial reads.
• Add specific instructions in your system prompt about how to analyze images (e.g., "When a user sends a rack photo, identify each device, manufacturer, and rack unit position from top to bottom.").

Slow response times#

Image analysis typically takes 5 to 15 seconds depending on complexity. For faster responses:

• Use smaller image files when possible.
• Consider GPT-5 Nano or Claude 3.5 Haiku for the speed-vs-accuracy trade-off.

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